ReefNet

Training metrics are Available in this Weights and Biases Report.

ReefNet is a RetinaNet implementation written in pure Keras developed to detect Crown-of-Thorns Starfish on the Great Barrier Reef, which pose an existential threat to the Great Barrier Reef due to a population decline of their most common predator. The traditional way of surveying Crown-of-Thorns Starfish is the “Manta Tow” method, where divers are towed along the reef, pausing ever so often to dive down and record how many COTS are visible. With such a large ocean bed, this method is clearly inefficient, unreliable, and costly.

More information about the problem Crown-of-Thorns Starfish pose to the Great Barrier Reef as well as efforts to control their population can be found in our project write up.

ReefNet constists of custom tf.data.Dataset loader, a keras.Model subclass implementation of RetinaNet, train test splitting for the dataset, integration with the kerascv.COCOMeanAveragePrecision and kerascv.COCORecall metrics, and a Keras callback to visualize predictions.

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